Machine learning models for vehicle accident potential injury detection

ABSTRACT

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and/or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and/or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.

TECHNICAL FIELD

The present disclosure relates to software development and deployment.In particular, the present disclosure describes techniques forgenerating and training cloud-based machine learning models, and eventsystems used to trigger execution of the models.

BACKGROUND

When accidents occur for vehicles in a driving environment, it can bevaluable for law enforcement, medical care providers, insuranceproviders, and other entities to quickly and accurately determine anypotential injuries that may have resulted to passengers and/or otherparties. Failures to accurately detect or predict potential injuriesresulting from vehicle accidents may cause delayed medical care andincreased risks of bad medical outcomes for individuals that may beinjured. Additionally, failing to quickly identify injuries andpotential injuries resulting from a vehicle accident can cause timedelays and additional processing costs associated with completing policereports, processing insurance claims, and the like. The existingtechniques for identifying injuries associated with vehicle accidentsoften involve time-consuming and computationally complex processes,which may be error-prone and may suffer from various other significantdrawbacks.

For example, existing techniques for detecting injuries resulting fromvehicle accidents often require manual inspection of the accident sceneand/or personal interviews with the parties involved. A party to anaccident may initially report the accident to a medical provider, lawenforcement organization, insurer, or the like. After reporting theaccident, the reporting party may be interviewed or given a medicalexamination, at which point the person conducting the interview orexamining the party may determine whether or not that individual isinjured. However, such interviews or examinations can be difficult toconduct quickly and accurately, and may be inadequate for determiningall of the potential injuries resulting from the accident. For instance,systems that rely on interviews and/or examinations with the parties toan accident may fail to take into account additional relevant data fromother data sources, including statements from other parties or witnessesto the accident, police reports, vehicle telematics, and other data fromthird-party data sources. Existing systems that determine accidentinjuries based on interviews and/or examinations with the partiesinvolved, without receiving or analyzing the relevant data from theseadditional data sources, may fail to accurately detect the full range ofinjuries and potential injuries resulting from the accident.

Complicating the analysis further is the fact that some injuriesresulting from an accident may be immediately apparent, while others maybe unreported, concealed, and/or otherwise unknown to the parties untillong after the accident has occurred. For instance, when a partyinvolved in an accident first reports that the accident has occurred, heor she may be unaware of any potential injuries to the other parties.Additionally, an individual may state shortly after an accident that heor she is uninjured, but may subsequently experience delayed symptoms ofnausea, neck pain, or other injuries that were not present immediatelyafter the accident. Therefore, existing techniques or systems thatevaluate accident data and determine potential injuries shortly after anaccident may fail to detect late-reported injuries and/or other injuriesthat are not immediately apparent. In contrast, systems that wait toanalyze and process accident data can lead to delayed detection ofpotential injuries, resulting in higher-risk medical outcomes andadditional time and expense when processing the accident data by lawenforcement, insurance providers, etc.

Further, various types of relevant vehicle accident data can be receivedfrom the various data sources over different communication channelsand/or at different times, including shortly after an accident hasoccurred or significantly later. Each of these data sources may providerelevant data for analyzing vehicle accidents in some cases but notothers, and different combinations of data sources and data types may beused to determine the potential injuries for different accidents. As aresult, existing systems that rely on a fixed number of data sourcesfrom which to receive and analyze accident data, and/or systems that usefixed rule-based techniques, may fail to accurately detect potentialinjuries.

Therefore, improved techniques are needed for quickly and accuratelydetermining the potential injuries that may result from a vehicleaccident, and for initiating specific actions on target computer systemsbased on the potential injury determinations.

SUMMARY

To address these and other problems and deficiencies in existingtechnologies, this disclosure describes systems and techniques fortraining and executing machine learning models configured to determineinjury probabilities based on vehicle accident data. In some examples, atraining component may construct a machine learning model (e.g., adecision tree model) including various branching criteria based onparticular vehicle accident data and ground truth injury data, and leafnodes storing injury probabilities associated with decision treebranches. A model execution component may execute a trained decisiontree model to determine the probability of a potential injury associatedwith a vehicle accident. Based on the potential injury probability thevehicle accident analysis system may identify one or more targetentities and/or target computer systems, and may determine automatedprocesses to initiate on the target systems based on the injuryprobability.

In various examples, the system may be implemented as an event-drivensystem, and/or may include cloud-based data storage and servicesconfigured to receive, store, and process accident data events. Anynumber of data sources may operate independently to retrieve and provideaccident data to a vehicle accident analysis system. When new or updatedaccident data is received from a data source, the system may update avehicle accident store and/or trigger an event to initiate an execution(or re-execution) of a machine learning model to determine (or update)the injury probability associated with the accident. In some cases, thetype of accident data received, the data source, and/or the timing ofreceiving the accident data may determine when and how often the trainedmodel is executed. For instance, the vehicle accident analysis systemmay include a deduplication component and/or may implement various delaytimers in response to receiving updated accident data, thereby controlthe timing and conditions under which the machine learning model may beexecuted.

Further, in various examples the vehicle accident analysis system mayevaluate and apply injury probability thresholds for routingcommunications and providing instructions to various target systems. Forinstance, an injury probability determined for a vehicle accident basedon a decision tree model can be compared to any number of probabilitythresholds associated with various target systems and/or processes thatmay be invoked on the target systems. The injury probability thresholdsassociated with the target systems and/or processes can be modifieddynamically based on the current workload metrics of each target system,thereby providing a workload control mechanism to allow the analysissystem to manage and coordinate the downstream processing of variousindependent target systems.

In an example of the present disclosure, a computer-implemented methodincludes receiving, by a computer system and via a first data channel,data associated with a vehicle accident, and generating, by the computersystem, a data payload based at least in part on the data associatedwith the vehicle accident. The method in this example also includesproviding, by the computer system, the data payload as input to amachine learning model, and receiving, by the computer system, an outputfrom the machine learning model. Additionally, the method includesdetermining, by the computer system, an injury probability associatedwith the vehicle accident, based at least in part on the output from themachine learning model, and determining, by the computer system, atarget computer system of an entity associated with the vehicleaccident, based at least in part on the injury probability associatedwith the vehicle accident. The method in this example also includestransmitting, by the computer system, an instruction to the targetcomputer system to initiate a process.

In another example of the present disclosure, a computer systemcomprises one or more processors, and one or more non-transitorycomputer-readable media storing computer-executable instructions that,when executed by the one or more processors, cause the one or moreprocessors to perform various operations. The operations in this exampleinclude receiving, via a first data channel, data associated with avehicle accident, retrieving, from an accident data store, previous dataassociated with the vehicle accident, and generating a payload based atleast in part on the data associated with the vehicle accident and theprevious data associated with the vehicle accident. The operations inthis example further include executing a machine learning model, theexecuting comprising providing the payload as input to the machinelearning model, and determining an injury probability associated withthe vehicle accident, based at least in part on an output from themachine learning model. Additionally, the operations in this exampleinclude determining a target computer system of an entity associatedwith the vehicle accident, based at least in part on the injuryprobability, and transmitting an instruction to the target computersystem to initiate a process.

Yet another example of the present disclosure includes one or morenon-transitory computer-readable media storing instructions executableby a processor, wherein the instructions, when executed, cause theprocessor to perform various operations. The operations in this exampleinclude receiving, via a first data channel, first data associated witha vehicle accident, receiving, via a second data channel different fromthe first data channel, second data associated with the vehicleaccident, and generating a data payload based at least in part on thefirst data associated with the vehicle accident and the second dataassociated with the vehicle accident. The operations in this examplefurther include providing the data payload as input to a machinelearning model, receiving an output from the machine learning model, anddetermining an injury probability associated with the vehicle accident,based at least in part on the output from the machine learning model.Additionally, the operations in this example include determining atarget computer system of an entity associated with the vehicleaccident, based at least in part on the injury probability, andtransmitting an instruction to the target computer system to initiate aprocess associated with the vehicle accident.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example vehicle accident analysis system includingcomponents configured to train and execute machine learning models basedon vehicle accident data, and to initiate various processes in targetsystems based on injury probabilities, in accordance with one or moreexamples of the present disclosure.

FIG. 2 illustrates another example vehicle accident analysis systemconfigured to train decision tree machine learning models based onvehicle accident data, ground truth injury data, and/or other datasources, in accordance with one or more examples of the presentdisclosure.

FIG. 3 illustrates another example vehicle accident analysis systemconfigured to receive data events associated with a vehicle accident,and execute a train decision tree machine learning model to determine aninjury probability associated with the vehicle accident, in accordancewith one or more examples of the present disclosure.

FIG. 4 illustrates an example decision tree model, including branchingcriteria based on vehicle accident data and output nodes correspondinginjury probability data, in accordance with one or more examples of thepresent disclosure.

FIG. 5 illustrates an example process of training machine learningmodels to output injury probability data based on vehicle accident datafrom various data sources, in accordance with one or more examples ofthe present disclosure.

FIG. 6 illustrates an example process of executing a machine learningmodel based on vehicle accident data, and determining and initiatingprocesses on target systems based on the model output, in accordancewith one or more examples of the present disclosure.

FIG. 7 is an example architecture of a computer server capable ofexecuting program components for implementing various techniquesdescribed herein.

FIG. 8 depicts an example computing environment including a vehicleaccident analyzer configured to create and deploy cloud-based systemsfor training and/or executing machine learning models for vehicleaccident analysis, in accordance with one or more examples of thepresent disclosure.

DETAILED DESCRIPTION

FIG. 1 depicts an example of a computing environment 100 including avehicle accident analyzer 102 configured to train and execute machinelearning (ML) models to detect potential injuries based on vehicleaccident data. As shown in this example, the vehicle accident analyzer102 may train and/or execute ML models 104 (e.g., decision tree MLmodels) to predict injury probabilities based on vehicle accident datareceived from various data sources 106-110. The data sources 106-110 mayoperate separately and independently from one another, to receive and/ordetermine relevant data associated with a vehicle accident, and mayprovide the accident data via over one or more data channels (e.g.,communication channels, network protocols, and/or communication networks112) to the vehicle accident analyzer 102. The vehicle accident analyzer102 may filter and partition the relevant accident data, and then maystore the accident data in a vehicle accident data store 114. Asdescribed in more detail below, the vehicle accident analyzer 102 mayuse various combinations of data received from the data sources 106-110and/or retrieved the data store 114 to train and update ML models 104,as well as to execute the ML models 104 to determine injuryprobabilities associated with particular vehicle accidents.

As described above, it may be useful for any number of entities,including medical care providers, law enforcement, and/or insuranceproviders, to receive quick and accurate information regarding thepotential injuries incurred during a vehicle accident. However, existingtechniques for obtaining injury data associated with vehicle accidentsmay be costly, time-consuming, and error-prone. For instance, one ormore parties to an accident may be unavailable for interviewing, and/ormay conceal or fail to report injuries obtained during the accident.Additionally, for certain types of injuries, the injured party might notinitially experience symptoms, but might only become aware of the injuryin the days or weeks following the accident.

As a result, the trained ML models 104 described herein may providequicker and more accurate determinations of the likelihood that avehicle accident may result in potential injuries to the partiesinvolved. ML models 104, which may be implemented as decision treemodels in some examples, can receive input data including analyses ofthe statements from parties or witnesses, vehicle telematics data,analyses of police reports or other accident documents, accident imagedata, accident location data, weather and traffic data associated withthe accident, etc. Based on a payload of relevant input data associatedwith an accident, the ML models 104 can output data indicating theprobability that the accident resulted in one or more injured parties.The vehicle accident analyzer 102 may use injury probability datareceived from the ML models 104 to determine particular target entitiesand/or target systems (e.g., medical provider servers, law enforcementservers, insurance provider systems, client devices of parties to theaccident, etc.), and may transmit data to and/or initiate processeswithin selected target systems.

To receive accident-related data from the data sources 106-110, thevehicle accident analyzer 102 may use an application programminginterface (API) layer 116, including one or more APIs associated witheach of the independent data sources 106-110. In some examples,different ML models 104 may be configured to receive different sets ofinput data (or payloads). An input payload for an ML model 104 mayinclude any combination of the various input data described herein,including any data received from data sources 106-110 and/or additionaldata retrieved from a vehicle accident data store 114. After receivingaccident data from one or more data sources 106-110, the vehicleaccident analyzer 102 may select an ML model 104 to be executed, forexample, based on the attributes of the accident such as thestate/region of the accident, the time when the accident occurred, thevehicle type, the customer and/or account type, etc. The vehicleaccident analyzer 102 may generate a payload of accident-related datacompatible with the inputs of the selected ML model 104, and may executethe ML model 104 and provide the payload as input to the ML model 104.

The data sources 106-110 may be implemented as independent computersystems configured to provide specific types and/or ranges ofaccident-related data to the vehicle accident analyzer 102. Each of thedata sources 106-110 may provide one or more particular types ofaccident data, which may include unique data or may overlap with thedata provided by additional data sources. Any of the data sources106-110 may provide an individual transmission of accident-related datato the vehicle accident analyzer 102, or may provide multipletransmissions of data at different times. The vehicle accident analyzer102 may receive accident data from the data sources 106-110 according toa predetermined data delivery schedule, or may receive unexpecteddeliveries of accident-related data from the data sources 106-110 at anytime.

In some examples, the vehicle accident analyzer 102 may include anevent-driven system, in which the vehicle accident analyzer 102subscribes to receive accident data “events” (e.g., transmissions of newor updated accident-related data) from each of the data sources 106-110.When an accident data event is received, the vehicle accident analyzer102 may store the updated accident data in the vehicle accident datastore, and may trigger the execution of the appropriate ML model(s) 104to determine updated injury probabilities for the accident. In suchexamples, any or all of the components of the event-driven system,including the data sources 106-110, vehicle accident analyzer 102,vehicle accident data store 114, event queues, buffers, etc., may becloud-based components implemented within a public cloud, private cloud,and/or hybrid cloud environment. Additionally or alternatively, any orall of these components may be implemented in on-premise data centers orservers, or in other non-cloud computing environments.

Although FIG. 1 depicts only three examples of data sources 106-110, itcan be understood from the context of this disclosure that any number ofindependent data sources may be used to provide various accident-relateddata to the vehicle accident analyzer 102. In this example, the datasource 106 may represent one or more data sources providing reportsand/or statements from the parties or witnesses to a vehicle accident.For example, the data source(s) 106 may be associated with medicalproviders, first responders, insurance representatives, etc., and mayprovide transcripts of written or recorded statements made by theparties involved or witnesses to an accident. The reports or statementsprovided by the data source(s) 106 also may include image data and/orvideo recordings of the individuals providing the statements. In somecases, instead of (or in addition to) transcripts or recordings, thedata source 106 may provide second-hand statements such as the notestaken by an interviewer or examiner (e.g., an emergency medicaltechnician, nurse, doctor, police officer, insurance agent orrepresentative, etc.) during a discussion with the party or witness tothe accident. As described below, the vehicle accident analyzer 102 mayreceive reports and/or statements associated with an accident from datasource(s) 106, and may analyze and extract data from the reports and/orstatements, including accident-related keywords, tone or emotionindicator data, user verification data, background noise, and the like.Based on the analysis and/or data extracted from these reports andstatements, the vehicle accident analyzer 102 may determine input datafor the ML models 104.

In this example, the data source 108 may represent one or more vehicletelematics data sources providing internal system data from one or morevehicles involved in an accident. For example, a telematics data source108 may provide the vehicle accident analyzer 102 with a set of internalstate data from a vehicle, during a time range associated with theaccident. A vehicle telematics system, for instance, may detect that anaccident has occurred and may store the time and location (e.g., GPScoordinates and/or street data) associated with the accident, as well asadditional vehicle state data corresponding to time and location of theaccident. The vehicle state data provided by a data source 108 mayinclude, for example, the vehicle speed, steering angle, lateral and/orlongitudinal acceleration, impact location on the vehicle, and/or thecontrol commands (e.g., steering, braking, acceleration, horn, lights,signals, etc.) performed by the vehicle before, during, and after theaccident. Vehicle state data also may include the number of passengersat the time of the accident, the types of passengers (e.g., adults orchildren, etc.), and/or where in the vehicle each passenger was sitting.As described below, the vehicle accident analyzer 102 may receive anycombination of telematics data from vehicle telematics data source(s)108, for a single vehicle or multiple vehicles involved in an accident,and may use the telematics to determine input data for the ML models104.

In this example, the data source 110 may represent one or morethird-party data sources providing additional relevant data associatedwith a vehicle accident. For instance, data source(s) 110 may includepublic or private data sources providing weather data, traffic data,visibility data, road surface data, road conditions data, and the like,associated with a vehicle accident. Additional data source(s) 110 mayinclude data identifying an exact time and/or location of the accident(e.g., a particular road, lane, shoulder, etc.). Data source(s) 110 alsomay include third-party data sources providing image or video dataassociated with the accident (e.g., traffic cameras, security cameras,dashboard camera videos from nearby vehicles, mobile device pictures orvideos captured by witnesses, etc.). As described below, the vehicleaccident analyzer 102 may receive any combination of the data receivedfrom third-party data source(s) 110, and may use the data to determineinput data for the ML models 104.

In various examples, any of the data sources 106-110 may provide data tothe vehicle accident analyzer 102 in response to requests (e.g., aweb-based request or web services request) from the vehicle accidentanalyzer 102, or proactively using an event-driven architecture in whichthe vehicle accident analyzer 102 subscribes to receive particular typesof data events from the data sources 106-110. When the vehicle accidentanalyzer 102 receives data from the data sources 106-110, it maydetermine a vehicle accident associated with the data (e.g., based on avehicle identifier, party/witness name, time, location, etc.) and storethe data within the vehicle accident data store 114. As described below,the vehicle accident analyzer 102 may use a combination of new/updateddata received from the data sources 106-110 and previous data stored inthe vehicle accident data store 114, to generate a data payload that canbe used as input to an ML model 104. Therefore, the vehicle accidentanalyzer 102 may execute and re-execute an ML model 104 multiple timesas new/updated data is received via the data source(s) 106-110, todetermine updated injury probabilities for an accident based on thenew/updated data.

Using a combination of accident-related data received from the datasources 106-110 and data stored in the vehicle accident data store 114,the vehicle accident analyzer 102 may train and execute ML models 104 todetermine the injury probabilities associated with particular vehicleaccidents. As described below in more detail, the vehicle accidentanalyzer 102 may include a model training engine 118 to train and updateML models 104, a deduplication component 120 to determine and implementdelay timers that to control the timing and conditions for executing theML models 104, and a model execution engine 122 to generate datapayloads associated with particular accidents and execute the ML models104 based on the data payloads.

After executing the ML model(s) 104 to determine an injury probabilityassociated with an accident, the vehicle accident analyzer 102 may usethe injury probability to determine various downstream processes toinitiate and/or the various target entities and systems on which thedownstream processes are to be initiated. For instance, based on theinjury probability output by an ML model 104 for a particular vehicleaccident, the vehicle accident analyzer 102 may select one or moreparticular target systems 126-132, and particular instructions/data tobe transmitted to the selected target systems 126-132 to initiateprocessing tasks associated with the accident on the target systems. Theoutputs from the vehicle accident analyzer 102 to the target systems126-132 may take the form of the notifications, messages, web serviceinvocations, API calls, and the like, any or all of which may causeprocessing tasks to be executed in the selected target system 126-132.In some examples, the vehicle accident analyzer 102 also may provide auser interface layer 124 included various target-specific userinterfaces to allow users of the target devices to view and/or updatevarious accident data, payload data, configure or re-execute the MLmodels 104, etc.

Although FIG. 1 includes four examples of target systems 126-132 thatmay be accessed and controlled by the vehicle accident analyzer 102 inresponse to the execution of the ML models 104, it can be understoodfrom the context of this disclosure that any number of additional targetsystems may be used in other examples. In this example, the targetsystem 126 may represent one or more client devices associated with theparties involved in the vehicle accident. Target system(s) 126 mayinclude, for example, desktop or laptop computers, tablet computers,smartphones and/or other mobile devices (e.g., wearable devices, etc.).Based on the injury probabilities determined for an accident, thevehicle accident analyzer 102 may initiate a message (e.g., notificationand/or user interface) on a target system 126. Messages to targetsystems 126 can include requests to a party or other individual foradditional input data to be used by the ML models 104. Additionalmessages to target systems can invoke notifications or user interfacesincluding instructions for the party/individual to seek medical care,complete electronic forms relating to law enforcement, insurance claimprocessing, etc.

In this example, the target system 128 may represent one or more medicalcare provider systems associated with the parties involved in theaccident. Target system(s) 128 may include, for example, medicalprovider servers, web portals, etc., to allow the vehicle accidentanalyzer 102 to initiate medical care requests and/or uploadaccident-related information directly to a medical care provider system.For instance, based on the injury probabilities determined for anaccident, the vehicle accident analyzer 102 may initiate a medicalexamination request for a party involved in the accident, and/or mayprovide (e.g., via a secure upload) any relevant accident data from thevehicle accident data store 114 and/or data sources 106-110 to thetarget systems 128.

In this example, the target system 130 may represent one or moreinsurance provider systems associated with the parties involved in theaccident. Target system(s) 130 may include, for example, insuranceservers, insurance agent systems, web portals for entry ofinsurance-related data, etc. Based on the injury probabilitiesdetermined for an accident, the vehicle accident analyzer 102 may openand/or modify insurance claims automatically via the target systems 130,may route claims internally within the insurance provider to specificclaim-handling groups or representatives, etc. The vehicle accidentanalyzer 102 also may provide (e.g., via a secure upload) any relevantaccident data from the vehicle accident data store 114 and/or datasources 106-110 to the target system(s) 130.

In this example, the target system 132 may represent one or morethird-party systems associated with the vehicle accident. Targetsystem(s) 132 may include, for example, law enforcement servers, clientdevices of witnesses, claimants, and/or vehicle repair facilities,and/or other third-party entities associated with the accident. Based onthe injury probabilities determined for an accident, the vehicleaccident analyzer 102 may transmit potential injury data, party data,and/or any other accident-related data, to the various target system(s)132.

In various examples, the vehicle accident analyzer 102 may determine andimplement injury probability thresholds for any or all of the targetsystems 126-132. For instance, after using an ML model 104 to determinean injury probability associated with an accident, the vehicle accidentanalyzer 102 may compare the injury probability to one or moreprobability thresholds associated with each of the target systems126-132. Based on the comparison of the injury probability of theaccident to the probability thresholds of the target systems 126-132,the vehicle accident analyzer 102 may determine which target systems areto be contacted and/or which processes are to be invoked on the targetsystems. As an example, if a first execution of an ML model 104 based onaccident-related data outputs a first injury probability below aprobability threshold for initiating a medical exam, then the vehicleaccident analyzer 102 may determine that the medical provider targetsystem 128 is not to be contacted to request to the medical exam.However, after receiving additional accident-related data from one ormore data sources 106-110, the vehicle accident analyzer 102 mayre-execute the ML model 104 based on the updated data. In this example,if the re-execution of the ML model 104 outputs a second injuryprobability above the probability threshold for initiating a medicalexam, then the vehicle accident analyzer 102 may transmit instructionsto the medical provider target system 128 to request to the medicalexam.

As noted above, various injury probability thresholds may be determinedby the vehicle accident analyzer 102 for particular target systemsand/or particular processes to initiate on the target systems. Forinstance, a first injury probability for an accident may cause a firstprocess to be initiated on an insurance claim processing target system130, and a second different injury probability may cause a secondprocess to be initiated on the same target system 130. The vehicleaccident analyzer 102 also may modify the probability thresholdsassociated with the target systems and/or the target processes to beinvoked. For instance, based on the current workload of a particulartarget system (e.g., in terms of computing resources, memory, availablepersonnel, etc.), the vehicle accident analyzer 102 may raise or lowerthe injury probability thresholds associated with the target system. Byadjusting the probability thresholds for the target system, the vehicleaccident analyzer 102 may cause more or less accident-related processingtasks to be invoked on the target system, thereby providing a controlmechanism for managing and coordinating the downstream processingworkload across the target systems 126-132, and for prioritizing themost valuable accident-related processing tasks (e.g., corresponding tothe greatest probability of injuries) over less valuable processingtasks.

When an accident involves multiple vehicles, a single ML model 104 maybe trained to output an injury probability for the accident as a whole.In other examples, the vehicle accident analyzer 102 may train and useseparate ML models 104 (or may re-execute the same ML model 104 withdifferent input data) to determine the separate injury probabilitiesassociated with each of the vehicles involved in the accident.Additionally, for multi-vehicle accidents, vehicle accident analyzer 102may invoke different combinations of target systems 126-132 and/ordifferent processes depending on the injury probabilities associatedwith the different vehicles. As an example, if the ML model(s) 104executed for a multi-vehicle accident indicate a relatively high injuryprobability associated with a first vehicle and a relatively low injuryprobability associated with a second vehicle, then the vehicle accidentanalyzer 102 may invoke one set of processes on particular targetsystems 126-132. However, in this example, if the ML model(s) 104indicated a relatively low injury probability for the first vehicle anda relatively high injury probability for the second vehicle, then thevehicle accident analyzer 102 may invoke a different set of processes ona different set of target systems 126-132. Additionally, the injuryprobability thresholds determined and stored by the vehicle accidentanalyzer 102 for the target systems 126-132 and/or target processes tobe invoked, also may include separate probability thresholds associatedwith different vehicles in a multi-vehicle accident and/or individualpassengers within each vehicle.

As illustrated by the examples above, the system depicted in FIG. 1 mayprovide technical advantages in operating vehicle accident analysissystems, and more generally in training and executing machine learningmodels in event-based computing infrastructures. For example, thevehicle accident analyzer 102 can train and execute machine learningmodels to quickly and accurately identify potentially injured partiesbased on vehicle accident data. The vehicle accident analyzer 102 canuse various machine learning models to determine more accurateprobability predictions for accident injuries, based on payloads thatinclude combined data from various data sources. As a result, thevehicle accident analyzer 102 may improve overall vehicle safety andindividual health outcomes when vehicle accidents occur, through earlyand accurate detection of potential injuries, even when those injuriesmay be unreported, concealed, and/or unknown to the other partiesinvolved in the accident.

FIG. 2 depicts another example computing environment 200 including amodel training engine 118 configured to construct and train ML models104 based on various vehicle data, accident data, and/or ground truthinjury data. In some examples, the computing environment 200 may usesimilar or identical components to those in computing environment 100discussed above, in which the same (or a similar) model training engine118 may be implemented within a vehicle accident analyzer 102. In otherexamples, the computing environment 200 may be different from thecomputing environment 100, and the model training engine 118 and modelexecution engine 122 may be implemented in separate computingsystems/environments. For instance, the model training engine 118 and/orvehicle accident data store may be implemented within a cloud-basedcomputing environment, while the model execution engine 122 may operatein an on-premise data center associated with the vehicle accidentanalyzer 102.

The model training engine 118 may generate and train ML models 104 basedon various sources of vehicle accident-related data, including vehicledata 202, accident data 204, driving environment data 206, and/or groundtruth injury data sources 208. As noted above, various vehicle-relatedand/or accident-related data may be received from various external datasources, or may be retrieved from the vehicle accident data store 114.In this example, vehicle data 202 may include attributes associated witha vehicle involved in an accident, such as the make, model, year, trim,and/or additional features of the vehicle. Accident data 204 may includeany of the accident-related data described above in reference to FIG. 1, including (but not limited to) any of the data received from the datasources 106-110. Driving environment data 206 may, for example, weatherdata, traffic data, visibility data, road surface data, and/orimage/video data associated with an accident. Finally, ground truthinjury data may include records from medical providers, law enforcementdata, insurance reports, etc., identifying injuries to individualsinvolved in vehicle accidents. For a previous vehicle accident (or anynumber of previous vehicle accidents), the combination of data 202-208associated with the previous accident(s) may be received by the modeltraining engine 118, and used to train one or more ML models 104 tooutput injury probabilities associated with accidents.

As shown in this example, the ML models 104 trained by the modeltraining engine 118 may include decision tree ML models. A decision treeML model 104 may comprise a tree structure having accident-relatedbranching criteria associated with each branching point in thestructure, and a distinct injury probability associated with each leafnode in the structure. In various examples, any number of differentvariants of decision tree algorithms may be used to implement the MLmodel 104, including but not limited to Iterative Dichotomiser 3 (ID3),Classification And Regression Tree (CART), Chi-square automaticinteraction detection (CHAID), multivariate adaptive regression splines(MARS), and/or Conditional Inference Trees. It can also be understoodfrom the context of this disclosure that in other examples the modeltraining engine 118 may implement ML models 104 using various additionalor alternative machine learning techniques and algorithms. For instance,the ML models 104 may include artificial neural network data structureshaving one or more levels, different node configurations, randomlyassigned initial node weights, and the model training engine 118 may useone or more regression algorithms, instance-based algorithms, Bayesianalgorithms, clustering algorithms artificial neural network algorithms,and/or deep learning algorithms, to train the ML models 104. DifferentML models 104 also may implement different machine-leaming algorithmsincluding, but not limited to, regression algorithms instance-basedalgorithms, Bayesian algorithms, clustering algorithms, association rulelearning algorithms, deep learning algorithms supervised learning,unsupervised learning, semi-supervised learning, etc.

The model training engine 118 may generate and train a single ML model104 to determine injury probabilities based on accident input data, ormay generate and train multiple ML models 104 corresponding to differenttraining data sets. As shown in this example, the model training engine118 has generated a number of different ML models 104, including MLmodel 210, ML model 212, and ML model 214. In this example, thedifferent ML models 210-214 may be based on different sets of trainingdata corresponding to different time periods, different vehicle types,different driving regions, different weather conditions, differentdriving scenarios, and/or different accident types, etc. In suchexamples, the model training engine 118 may generate and train multipleML models 104 to apply to different accident scenarios or attributes,and the model execution engine 122 may select and execute an appropriateML model 104 based on the characteristics of the accident.

As described below in more detail, each ML model 104 may have anassociated set of input data (or payload) determined by the modeltraining engine 118. For instance, during the training process for adecision tree model 104 (or other ML model type), the machine learningalgorithms of the model training engine 118 may determine whichaccident-related data is relevant (e.g., outcome determinative) topredicting potential injuries associated with the accident. The relevantaccident-related data may be identified and used to determine thebranching criteria for the decision tree ML model 104 (or input data forother ML model types, etc.). Other accident-related data may bedetermined by the model training engine 118 to be irrelevant (or lessrelevant) as a predictor of potential injuries from an accident, andthis irrelevant (or less relevant) data may be excluded as input datafrom the ML model 104.

A data payload (or payload) may refer to the set of relevant accidentdata provided as input to an ML model 104 configured to output injuryprobability data associated with the accident. It can be understood thatdifferent ML models 104 may use different combinations ofaccident-related data as input, and thus may have different payloads. Insome examples, the model execution engine 122 may determine which MLmodel 104 to execute for an accident, by comparing the differentpayloads of the ML models 104 to the set of data that is currentlyavailable for an accident. For instance, when an initial set ofaccident-related data is received shortly after an accident hasoccurred, the model execution engine 122 may select and run a first MLmodel 104 for which the initial accident data set satisfies the datapayload. In examples in which additional accident-related data isreceived for the same accident (e.g., at a later time), the modelexecution engine 122 may select and run a second and more robust MLmodel 104 having a larger payload and greater performance in predictingpotential injuries.

FIG. 3 depicts another example computing environment 300 including adeduplication component 120 (which may be similar or identical to thededuplication component 120 depicted in FIG. 1 ) configured to controlthe timing and frequency of executions of the ML models 104. Asdescribed below, the deduplication component 120 may implement delaytimers and/or event buffers based on updated accident data from the datasources 106, to control the timing and conditions under which the MLmodels 104 may be executed. Accordingly, the deduplication component 120can be configured to save computational resources and improve theefficiency of the vehicle accident analyzer 102, by optimallydetermining when and how often ML models 104 are executed in response toreceiving updated accident data.

As shown in this example, the deduplication component 120 can be usedwithin event-driven systems and architectures. However, a deduplicationcomponent 120 may be used similarly or identically in other types ofsystems as well (e.g., non-event systems). In an event-driven system,the vehicle accident analyzer 102 may subscribe to and receive new orupdated accident-related data as data events. Data events including newor updated accident data can be provided by any number of data sources302, such as data sources 106-110 (which may be the same or similar asthe data sources 106-110 depicted in FIG. 1 ). As noted above, althoughsome accident data may be available shortly after an accident hasoccurred, other accident data such as party or witness statements,police reports, medical reports, driving conditions data, vehicletelematics data, and the like, might not be immediately available afteran accident. Instead, this delayed accident data may be received via theindependent data sources 106-110, at unpredictable and/or inconsistenttimes that may range between a few minutes and several months after anaccident has occurred.

In some event-driven systems, a vehicle accident analyzer 102 may beconfigured to re-execute one or more ML models 104 in response toreceiving new or updated accident-related data from the data sources106-110. Although re-executing the ML model(s) 104 in response toreceiving updated accident data may improve the accuracy of thedetermined injury probabilities, excessive re-execution of the MLmodel(s) 104 may use significant computing resources and reduce theefficiency and workload capabilities of the vehicle accident analyzer102. As an example, within a single statement from a party or witness toan accident, or a single batch of telematics data, many differentaccident data fields may be updated in the vehicle accident data store114. In this example, the vehicle accident analyzer 102 were tore-execute an ML model 104 each time a data field is updated, the numberof re-executions may be unnecessarily excessive, and the hardware andsoftware resources of the vehicle accident analyzer 102 may be quicklydepleted causing system errors and failures.

To address these problems, the deduplication component 120 may preventthe vehicle accident analyzer 102 from re-executing the ML model(s) 104after each accident-related data event. As shown in this example, whenthe vehicle accident analyzer 102 receives an event record 304 from anaccident-related data source 302, the deduplication component 120 mayanalyze the event with an accident event processor component 306. Theaccident event processor component 306 may use the type of data receivedin the event record 304, the data source 302 from which the event record304 was received, and/or the relative timing of the event record 304compared to other received event records, to determine whether or not toexecute an ML model 104 based on the received event record 304.

In some cases, based on the type and amount of accident data received,the data source 302 providing the data, and/or the timing of the data,the accident event processor component 306 may immediately invoke themodel execution engine 122 to execute (or re-execute) the ML model(s)104 to update the injury probability for the accident. In other cases,the accident event processor component 306 may determine that the MLmodel(s) 104 should not be executed immediately in response to the eventrecord 304. For instance, the accident event processor component 306 maydetermine that the data in the event record 304 is unlikely tosignificantly change the injury probability output by the ML model(s)104 for the accident. Additionally or alternatively, the accident eventprocessor component 306 may determine that there is a likelihood thatadditional accident-related data will be received in the near future. Inthese instances, the accident event processor component 306 mayinstantiate a delay timer 308 and/or may store the event recordtemporarily in an event buffer 310. During the duration of the delaytimer 308 (e.g., 5 seconds, 10 seconds, 15 seconds, etc.), anyadditional event records 304 received by the vehicle accident analyzer102 may be stored in the event buffer 310. When the delay timer 308completes, the accident event processor component 306 may store any newor updated accident data from the event buffer 310 into the vehicleaccident data store 114, clear the event buffer 310, and invoke themodel execution engine 122 to execute (or re-execute) the ML model(s)104 to update the accident injury probability based on the new orupdated data records.

FIG. 4 shows an example decision tree model 400, including branchingcriteria based on vehicle accident data and output nodes correspondinginjury probability data, in accordance with one or more examples of thepresent disclosure. In some examples, any or all of the ML models 104described herein may be implemented as a decision tree model 400. Insuch examples, decision tree ML models may provide particular efficiencyadvantages when evaluating accident data to determine injuryprobabilities. However, in other examples, the ML models 104 may beimplemented using artificial neural network structures or any other typeof machine learning models and/or algorithms.

As shown in this example, the decision tree model 400 may include a treedata structure including a number of branching nodes having associatedbranching criteria, and leaf nodes storing associated injury probabilitydata. Each of the branching criteria in this example may be based on ormore of the accident-related data described herein, and may provide abinary (or non-binary) threshold for determining which branch to followwhile evaluating the accident data. For instance, in this example, thefirst branching node 402 may represent a branching criteria based onwhich party provided the accident data (e.g., the party associated withthe data channel over which the accident data was received). The secondbranching node 404 may represent a branching criteria based on the timethe accident was reported relative to the time that the accidentoccurred. The third branching node 406 may represent a branchingcriteria based on the presence or absence of a particular keyword in theaccident description provided by a party to the accident. Examples ofkeywords that can be used as branching criteria within a decision treemodel 400 may include, for instance, keywords (or phrases) such as“rolled over,” “sliding,” “head-on,” “broken glass,” etc. Finally, inthis example, the leaf node 408 may store one possible output of themodel (e.g., an injury probability) associated with the particularbranch of the decision tree defined by the particular branching criteriaat branching nodes 402-408. Although the example decision tree model 400shown in FIG. 4 depicts a simple model using binary branching and arelatively small number of nodes and levels, it can be understood thatany number and configuration of tree branches, accident-relatedbranching criteria, and/or injury probability outputs may be used inother examples.

FIG. 5 is a flow diagram illustrating an example process 500 of traininga machine learning model to output an injury probability associated witha vehicle accident, based on accident-related data from various datasources. In various examples, some or all of the operations of process500 may be performed by a model training engine 118 associated with avehicle accident analyzer 102. As described below, the model trainingengine 118 may use combinations of associated accident-related data andground truth injury data to train one or more ML models 104 to determineinjury probabilities associated with vehicle accidents. A model trainingengine 118 may be implemented within a vehicle accident analyzer 102, asdepicted in FIG. 1 , or may be implemented within a separate serverand/or computing environment. In various examples, the model trainingengine 118, and/or the data sources from which the training data isreceived, may be implemented in a public, private, or hybrid cloud in acloud computing environment, or may be implemented in an on-premise datacenter.

At operation 502, the model training engine 118 may receive historicalaccident data associated with one or more previous vehicle accidents. Insome examples, the model training engine 118 may receive the historicalaccident data from one or more of the data sources 106-110, and/or fromthe vehicle accident data store 114. Examples of the historical accidentdata may include statements, reports, and/or other information providedby the parties or witnesses to an accident. Additional examples ofhistorical accident data may include data reports or statements frommedical providers, first responders, insurance representatives, etc.,and may include transcripts of written or recorded statements made bythe parties involved or witnesses to an accident. Further examples ofhistorical accident data may include the vehicle specifications, vehicletelematics data, image or video data associated with the accident,and/or the driving conditions associated with the accident.

At operation 504, the model training engine 118 may receive ground truthinjury data associated with the same set of previous accidents for whichdata was received in operation 502. As discussed above, ground truthinjury data associated with previous accidents may include records frommedical providers, law enforcement data, insurance reports, and thelike. The ground truth injury data may include data identifyingdocumented injuries to individuals involved in the previous accidents.

At operation 506, the model training engine 118 may determine whetherthe historical accident data and ground truth data, received inoperations 502 and 504 respectively, are to be partitioned into multipletraining data sets. In some examples, the model training engine 118 mayuse a single training data set to train a single ML model 104 fordetermining injury probabilities based on any payload ofaccident-related data. In such examples, the model training engine 118may determine not to partition the historical accident data and groundtruth data into multiple data sets (506:No).

In other examples, the model training engine 118 may use multipledifferent sets of training data and may train multiple ML models 104. Inthese examples, each of the ML models 104 can be associated with one ormore classifications of vehicles, accidents, or injuries, etc. Forinstance, a first ML model 104 can be executed for accidents involving aparticular vehicle type, and a different ML model 104 can be executedfor accidents involving a different vehicle type. As another example, afirst ML model 104 can be executed for accidents occurring in aparticular state/region and in particular weather conditions, whiledifferent ML models104 can be executed for accidents occurring indifferent states/regions and in different weather conditions, etc. Inthese examples, the model training engine 118 may determine that thehistorical accident data and ground truth data are to be partitionedinto multiple data sets (506:Yes). Accordingly, at operation 508, themodel training engine 118 may determine one or more criteria forpartitioning the historical accident and injury data. Examples ofcriteria may include any combination of vehicle data, accident-relateddata (e.g., party or witness statement data, telematics data, drivingcondition data, etc.) and/or injury data. Then, at operation 510, themodel training engine 118 may generate the accident data partitions andpartition the data into multiple separate training data sets, each ofwhich may be used to train a separate ML model 104.

At operation 512, the model training engine 118 may train one or more MLmodels 104 based on the training data sets determined in operations 506and/or 510. In some examples, the model training engine 118 mayconstruct and train decision tree ML models comprising tree structureswith accident-related branching criteria, and injury probabilitiesassociated with each leaf node. However, in other examples, the modeltraining engine 118 may implement ML models 104 using various othermachine learning techniques and algorithms, including (but not limitedto) neural network-based ML models. The ML models 104 generated andtrained by the model training engine 118 may implement various differentmachine-leaming algorithms, such as regression algorithms instance-basedalgorithms, decision tree algorithms, Bayesian algorithms, clusteringalgorithms, association rule learning algorithms, deep learningalgorithms supervised learning, unsupervised learning, semi-supervisedlearning, etc.

At operation 514, the model training engine 118 may output and/or storethe decision tree model(s) generated and trained in operation 512. Thedecision tree model(s) may be stored as ML models 104 in a server (orother computer storage) associated with the vehicle accident analyzer102. In some cases, model training engine 118 may operate in onecomputing environment (e.g., a cloud-based environment) to receiveaccident-related data and train the models, and may provide the trainedmodels to a vehicle accident analyzer 102 computer system operating in aseparate computing environment (e.g., an on-premise datacenter) forexecution.

At operation 516, the model training engine 118 may determine payloadinformation associated with the trained ML model(s) 104, and may outputthe payload information to one or more systems associated with a modelexecution engine 122. As noted above, a data payload may include a setof accident-related data provided as input to the ML models 104.Different ML models 104 may have different payload data corresponding todifferent combinations of accident-related input data. In some examples,the model training engine 118 may provide payload information for eachML model 104 to the model execution engine 122, which may use thepayload information to determine which ML model(s) 104 to execute basedon the available accident data.

FIG. 6 is a flow diagram illustrating a process 600 of selecting andexecuting a trained ML model 104 configured to output an injuryprobability based on vehicle accident data, and using the model outputto determine and initiate processes on one or more target systems. Invarious examples, some or all of the operations of process 600 may beperformed by a vehicle accident analyzer 102 including a model executionengine 122 and/or a deduplication component 120. As described below, oneor both of the vehicle accident analyzer 102 and the model executionengine 122 may be implemented within a vehicle accident analyzer 102, asdepicted in FIG. 1 , or may be implemented within separate serversand/or separate computing environments. For instance, a deduplicationcomponent 120, a model execution engine 122 and/or a library of thetrained ML models 104 may be implemented in a public, private, or hybridcloud in a cloud computing environment, or may be implemented in anon-premise data center associated with the vehicle accident analyzer102.

At operation 602, the vehicle accident analyzer 102 may receive dataassociated with an accident involving one or more vehicles. In variousexamples, the accident-related data received in operation 602 may be aninitial reporting of a vehicle accident within an initial set ofaccident data, or may be new or updated accident data for a previouslyreported accident. Examples of the accident-related data that may bereceived in operation 602 can include statements, reports, and/or otherinformation provided by the parties or witnesses to the accident.Additional examples of accident-related data can include data reports orstatements from medical providers, first responders, insurancerepresentatives, etc., and may include transcripts of written orrecorded statements made by the parties involved or witnesses to anaccident. Further examples of accident data may include the vehiclespecifications, vehicle telematics data, image or video data associatedwith the accident, and/or the driving conditions associated with theaccident.

At operation 604, the vehicle accident analyzer 102 may determinewhether to immediately invoke or to delay execution of one or more MLmodel(s) 104 configured to determine an injury probability for theaccident based on the accident-related data. In some instances,operation 604 may be performed by a deduplication component 120. Asdescribed above, the deduplication component 120 may implement delaytimers and/or event buffers to control the timing and conditions underwhich the ML models 104 may be executed. When the vehicle accidentanalyzer 102 receives the accident-related data in operation 602, thededuplication component 120 may analyze the accident data, using anaccident event processor component 306 or similar component. In variousexamples, the deduplication component 120 may identify the type of theaccident data received in operation 602, the data source from which theaccident data was received, and/or the relative timing of the accidentdata compared to other receipts of accident data. Based on one or moreof the attributes of the accident data received in operation 602, thededuplication component 120 may determine in operation 604 whether toimmediately execute an ML model 104 based on the received accident data(604:No), or whether to implement a timer to delay or defer execution ofthe ML model 104 (604:Yes).

At operation 606, the vehicle accident analyzer 102 may instantiate adelay timer 308 to delay or defer execution of an ML model 104 untilafter the completion of the delay timer 308. For instance, thededuplication component 120 may determine that the accident datareceived in operation 602 is unlikely to significantly change the injuryprobability output by the ML model(s) 104, and/or that there is alikelihood that additional accident data will be received for the sameaccident in the near future. As a result, the deduplication component120 may instantiate a delay timer 308 in operation 606 and/or may storethe accident data received in operation 602 into an event buffer 310.When the delay timer 308 completes, the accident data received inoperation 602 (and any additional new or updated accident data) may becleared from the event buffer 310 and stored into the vehicle accidentdata store 114, after which the deduplication component 120 may invokethe model execution engine 122 to execute (or re-execute) the MLmodel(s) 104 for the accident.

At operation 608, the vehicle accident analyzer 102 may determine and/orretrieve a data payload of accident-related to provide as input to oneor more selected ML model(s) 104. As noted above, a data payload mayinclude a set of accident-related data provided as input to an ML model104. Different ML models 104 may have different payload datacorresponding to different combinations of accident-related input data.In some examples, the model training engine 118 may provide payloadinformation for each ML model 104 to the model execution engine 122,which may use the payload information to generate payloads for theexecuting ML model(s) 104. For instance, a payload of model input datamay include a combination of data fields based on the accident-relateddata received in operation 602 and additional (e.g., previouslyreceived) accident-related data for the same accident, which may beretrieved from the vehicle accident data store 114.

At operation 610, the vehicle accident analyzer 102 may execute theselected ML model(s) 104 to determine the injury probabilitiesassociated with the accident. In some examples, the vehicle accidentanalyzer 102 may use a model execution engine 122 configured to executethe selected ML model(s) 104, provide as input to the models thepayload(s) determined in operation 608, and then receive outputs fromthe models includes injury probabilities associated with the accident.In some examples, one or more of the ML model(s) 104 may includedecision tree models. In such examples, the model execution engine 122may be configured to traverse the branches of the decision tree model,evaluating each branching criteria based on the accident data, andreaching a leaf node having an associated injury probability value. Inother examples, the ML models 104 may include neural network-basedmodels and/or other machine learning models, in which the payload may beprovided to the input layer of the neural network.

At operation 612, the vehicle accident analyzer 102 may determine one ormore target entities and/or target systems associated with the accident.For instance, the target entities associated with a vehicle accident mayinclude the parties to the accident, witnesses, medical providers, lawenforcement, and/or insurance providers associated with the parties. Thetarget systems may include any computing systems or devices associatedwith these entities, such as medical provider servers, law enforcementservers, insurance provider systems, client devices of the parties tothe accident, etc. In various examples, determining the target systemsmay include determining the client device identifiers, server or domainnames or identifiers, web portal addresses associated with the targetentities, and the like.

At operation 614, the vehicle accident analyzer 102 may compare theinjury probability of the accident, determined based on the model outputoperation 610, to one or more probability thresholds associated with thetarget systems determined in operation 612. As discussed above, thevehicle accident analyzer 102 may determine and store variousprobability thresholds associated with target systems and/or particularprocesses to be invoked on the target systems. Based on the injuryprobability associated with the accident, and the various injuryprobabilities of a particular target system, the vehicle accidentanalyzer 102 may determine which processes (if any) to invoke on thetarget system. As an example, an injury probability for an accident thatis greater than a probability threshold (614:Yes) may cause the vehicleaccident analyzer 102 to initiate a process on a target system inoperation 616. In contrast, an injury probability for the accident thatis less than the probability threshold (614:No) may cause the vehicleaccident analyzer 102 not to initiate the process, after which theprocess 600 may proceed back to operation 612 to determine a nextpotential target system associated with the same accident.

At operation 616, the vehicle accident analyzer 102 may determine andinitiate the process(es) on the target system determined in operation612. As discussed above, the processes initiated on the target systems126-132 may include notifications, messages, web service invocations,API calls, and the like, any or all of which may cause processing tasksto be executed in the selected target system. For instance, based ondetermining that an injury probability associated with an accidentexceeds a probability threshold, the vehicle accident analyzer 102 mayinitiate a process via a party (or third-party) client device to provideadditional accident data for the ML models 104. Additional processesthat can be initiated on the target systems 126-132 may includeinstantiating alerts/notifications and/or other user interfacesinstructing a party to seek medical care, or to complete additionalelectronic forms relating to law enforcement, insurance claimprocessing, etc. Further examples of processes that can be initiated onvarious target systems 126-132 in operation 616 may include automatedrequests to schedule medical exams and/or initiate medical procedures,automated uploading of accident-related information directly to medicalcare provider systems, opening and/or modifying insurance claimsautomatically on a target system, automatically routing claimsinternally within an insurance provider to a specific claim-handlinggroup or representative, and/or initiating secure uploads of relevantaccident data from the vehicle accident data store 114 and/or datasources 106-110 to one or more target system(s) 130.

FIG. 7 shows an example architecture of a computer server 700 capable ofexecuting program components for implementing the various functionalitydescribed herein. Although the computer architecture in this example islabeled as a server, it can be understood from this disclosure thatsimilar or identical computer architectures may be implemented viaworkstations, desktop or laptop computers, tablet computers, networkappliances, mobile devices (e.g., smartphones, etc.) or other computingdevices, and/or virtual machines or cloud-based computing solutions, anyor all of which may execute any combination of the software componentsdescribed herein. The server 700 may, in some examples, correspond toany of the computing systems or devices described above, such as avehicle accident analyzer 102 including decision tree models 104 and/orvehicle accident data store 114, data source systems 106-110, targetsystems 126-132, and/or any other computing devices, systems, orcomponents executing the software components described herein. It willbe appreciated that in various examples described herein, a server 700might not include all of the components shown in FIG. 7 , may includeadditional components that are not explicitly shown in FIG. 7 , and/ormay utilize a different architecture from that shown in FIG. 7 .

The server 700 includes a baseboard 702, or “motherboard,” which may bea printed circuit board to which a multitude of components or devicesare connected by way of a system bus or other electrical communicationpaths. In one illustrative configuration, one or more central processingunits (“CPUs”) 704 operate in conjunction with a chipset 706. The CPUs704 can be standard programmable processors that perform arithmetic andlogical operations necessary for the operation of the server 700.

The CPUs 704 perform operations by transitioning from one discrete,physical state to the next through the manipulation of switchingelements that differentiate between and change these states. Switchingelements generally include electronic circuits that maintain one of twobinary states, such as flip-flops, and electronic circuits that providean output state based on the logical combination of the states of one ormore other switching elements, such as logic gates. These basicswitching elements can be combined to create more complex logiccircuits, including registers, adders-subtractors, arithmetic logicunits, floating-point units, and the like.

The chipset 706 provides an interface between the CPUs 704 and theremainder of the components and devices on the baseboard 702. Thechipset 706 can provide an interface to a RAM 708, used as the mainmemory in the server 700. The chipset 706 can further provide aninterface to a computer-readable storage medium such as a ROM 710 ornon-volatile RAM (“NVRAM”) for storing basic routines that help tostartup the server 700 and to transfer information between the variouscomponents and devices. The ROM 710 or NVRAM can also store othersoftware components necessary for the operation of the server 700 inaccordance with the configurations described herein.

The server 700 can operate in a networked environment using logicalconnections to remote computing devices and computer systems through anetwork, such as the network 718, which may be similar or identical toany of the communication networks discussed above. The chipset 706 alsomay include functionality for providing network connectivity through aNetwork Interface Controller (NIC) 712, such as a gigabit Ethernetadapter. The NIC 712 is capable of connecting the server 700 to othercomputing devices (e.g., operator devices, external software developmentenvironments, test systems, cloud-based deployment systems, etc.) overthe network 718. It should be appreciated that multiple NICs 712 can bepresent in the server 700, connecting the computer to other types ofnetworks and remote computer systems. In some instances, the NICs 712may include at least on ingress port and/or at least one egress port.

The server 700 can also include one or more input/output controllers 716for receiving and processing input from a number of input devices, suchas a keyboard, a mouse, a touchpad, a touch screen, an electronicstylus, or other type of input device. Similarly, an input/outputcontroller 716 can provide output to a display, such as a computermonitor, a flat-panel display, a digital projector, a printer, or othertype of output device.

The server 700 can include one or more storage device(s) 720, which maybe connected to and/or integrated within the server 700, that providenon-volatile storage for the server 700. The storage device(s) 720 canstore an operating system 722, data storage systems 724, and/orapplications 726, which may include any or all of the systems and/orcomponents described herein. The storage device(s) 720 can be connectedto the server 700 through a storage controller 714 connected to thechipset 706. The storage device(s) 720 can consist of one or morephysical storage units. The storage controller 714 can interface withthe physical storage units through a serial attached SCSI (“SAS”)interface, a serial advanced technology attachment (“SATA”) interface, afiber channel (“FC”) interface, or other type of interface forphysically connecting and transferring data between computers andphysical storage units.

The server 700 can store data on the storage device(s) 720 bytransforming the physical state of the physical storage units to reflectthe information being stored. The specific transformation of physicalstate can depend on various factors, in different embodiments of thisdescription. Examples of such factors can include, but are not limitedto, the technology used to implement the physical storage units, whetherthe storage device(s) 720 are characterized as primary or secondarystorage, and the like.

For example, the server 700 can store information to the storagedevice(s) 720 by issuing instructions through the storage controller 714to alter the magnetic characteristics of a particular location within amagnetic disk drive unit, the reflective or refractive characteristicsof a particular location in an optical storage unit, or the electricalcharacteristics of a particular capacitor, transistor, or other discretecomponent in a solid-state storage unit. Other transformations ofphysical media are possible without departing from the scope and spiritof the present description, with the foregoing examples provided only tofacilitate this description. The server 700 can further read informationfrom the storage device(s) 720 by detecting the physical states orcharacteristics of one or more particular locations within the physicalstorage units.

In addition to the storage device(s) 720 described above, the server 700can have access to other computer-readable storage media to store andretrieve information, such as program modules, data structures, or otherdata. It should be appreciated by those skilled in the art thatcomputer-readable storage media is any available media that provides forthe non-transitory storage of data and that can be accessed by theserver 700. In some examples, the various operations performed by thecomputing systems described herein (e.g., vehicle accident analyzer 102,data sources systems 106-110, target systems 126-132, etc.) may beimplemented within a datacenter including one or more servers or devicessimilar to server 700. For instance, some or all of the operationsdescribed herein may be performed by one or more server 700 operating ina networked (e.g., client-server or cloud-based) arrangement.

By way of example, and not limitation, computer-readable storage mediacan include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology. Computer-readable storage mediaincludes, but is not limited to, RAM, ROM, erasable programmable ROM(“EPROM”), electrically-erasable programmable ROM (“EEPROM”), flashmemory or other solid-state memory technology, compact disc ROM(“CD-ROM”), digital versatile disk (“DVD”), high definition DVD(“HD-DVD”), BLU-RAY, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired information ina non-transitory fashion.

As mentioned briefly above, the storage device(s) 720 can store anoperating system 722 utilized to control the operation of the server700. In some examples, the operating system 722 comprises a LINUXoperating system. In other examples, the operating system 722 comprisesa WINDOWS® SERVER operating system from MICROSOFT Corporation ofRedmond, Washington. In further examples, the operating system 722 cancomprise a UNIX operating system or one of its variants. It should beappreciated that other operating systems can also be utilized. Thestorage device(s) 720 can store other system or application programs anddata utilized by the server 700.

In various examples, the storage device(s) 720 or othercomputer-readable storage media is encoded with computer-executableinstructions which, when loaded into the server 700, transform thecomputer from a general-purpose computing system into a special-purposecomputer capable of implementing various techniques described herein.These computer-executable instructions transform the server 700 byspecifying how the CPUs 704 transition between states, as describedabove. In some examples, the server 700 may have access tocomputer-readable storage media storing computer-executable instructionswhich, when executed by the server 700, perform the various techniquesdescribed herein. The server 700 can also include computer-readablestorage media having instructions stored thereupon for performing any ofthe other computer-implemented operations described herein.

As illustrated in FIG. 7 , the storage device(s) 720 may store one ormore data storage systems 724 configured to store data structures andother data objects. In some examples, data storage systems 724 mayinclude one or more data stores, which may be similar or identical tothe vehicle accident data store 114 and/or even data stores associatedwith any number of the data sources 106-110. Additionally, the softwareapplications 726 stored on the server 700 may include one or more clientapplications, services, and/or other software components. For example,application(s) 726 may include any combination of the components 110-118and 124 discussed above in relation to the vehicle accident analyzer102, and/or other software components described above in reference toFIGS. 1-6 .

As discussed above, the various techniques described herein for trainingand executing machine learning models configured to determine injuryprobabilities based on vehicle accident data, can be implemented withinvarious different computing environments. In some examples, one or bothof a model training engine and/or model execution engine as describedherein may operate in a cloud-based environment (e.g., a public cloud,private cloud, and/or hybrid cloud environment) and/or in a separatenon-cloud computing environment (e.g., an on-premise datacenter). FIG. 8depicts one example of a cloud-based computing environment 800 fortraining and executing models to determine injury probabilities based onvehicle accident data.

In this example, the computing environment 800 includes one or more userdevices 802 associated with an organization for which cloud environmentsare to be created, provisioned, and managed. The user devices 802 may becontrolled by a deployment engineer or other operator associated withthe organization and may be implemented using, for example, a desktop orlaptop computer, tablet computer, mobile device, or any other computingdevice. The user devices 802 may include network interfaces andfunctionality capable of accessing the vehicle accident analyzer 804,and/or client software configured to access interface(s) provided by thevehicle accident analyzer 804 or cloud-based applications and service.The vehicle accident analyzer 804 in this example may be similar oridentical to the vehicle accident analyzer 104 described in the aboveexamples, and may be implemented using various different computingarchitectures, including one or more computing devices, servers, and/orother computing systems. Additionally, as shown in this example, thevehicle accident analyzer 804 may include various computing componentsto provide one or more interface(s), receive accident modelconfiguration variables from the user devices 802, and to create anddeploy cloud-based systems via cloud service providers.

In this example, the vehicle accident analyzer 804 includes an accidentmodel configuration interface 806 and a cloud provisioning system 808;however, the vehicle accident analyzer 804 also may include any othercomponents or features described herein for vehicle accident analyzer104. In this example, the accident model configuration interface 806 maybe accessible by the user devices 802 and/or other authorized clientdevices associated with the organization. In some examples, the userdevices 802 may execute a thin, web-based client (e.g., Internetbrowser) to access the accident model configuration interface 806 via asecure network. Additionally or alternatively, the accident modelconfiguration interface 806 may include a thick client in which some orall of the interface functionality may execute on the user devices 802.The accident model configuration interface 806 may include a graphicaluser interface (GUI), such as web browser-based or application-basedinterface through which a user or operator may provide data relating tothe constructing, training, and execution of models as described hereinfor determining the potential injury probabilities associated withvehicle accidents. In some examples, a GUI need not be implemented andthe accident model configuration interface 806 may correspond to aprogrammatic interface (e.g., API) or other non-graphical interface.

The accident model configuration interface 806 may include any interfacethrough which the vehicle accident analyzer 804 receives one or moreconfiguration data, including configuration data relating to the modelsto be trained and executed, and/or cloud environment configuration data(e.g., configuration variables) from an operator of the organization.For instance, such configuration variables may include any dataassociated with a configuration (and/or any other attribute) of a clouddeployment. For instance, configuration variables may include anyselection made by operator or any input provided during the creation,configuration, and/or deployment of a cloud environment. Examples ofconfiguration variables may include, but are not limited to, accountnames and/or alias associated with the environment, the organizationsand/or projects associated with the environment, and contact informationassociated with the environment (e.g., administrator(s), emailaddresses, etc.). Additional examples of configuration variables mayinclude the type/functionality of the environment (e.g., production ornon-production), and roles associated with the account (e.g.,identification of administrators, product developers, etc.). Additionalconfiguration variables may include the specifications for networkconstructs to be used in the environment, such as the IP addressesand/or configuration data of the virtual private network, routingtables, routing rules, security policies/groups, and/or network accesscontrollers associated with the environment. Additional examples ofconfiguration variables may include data identifying the cloud serviceprovider and/or cloud region for the environment, the continuousintegration and continuous deployment (CI/CD) pipeline associated withthe environment, the encryption component(s) to be used within theenvironment, the logging component(s) to be used, and/or any otherautomation components tools, or services to be used in the environment.It can be understood from the context of this disclosure that theexamples of configuration variables described herein are illustrativeand non-limiting, and that any attribute of a clouddeployment/environment may be represented as configuration variable insome examples.

It can be understood in the context of this disclosure that differentconfiguration data may be used to create and deploy different types ofmodels for determining potential injury probabilities associated withvehicle accidents, including models trained using different trainingdata sources, different sets of model training/testing data, and/ordifferent model training algorithms. Additional configuration datareceived via the accident model configuration interface 806 may be usedto determine the execution parameters for such models, including theinput data types, delay timers, etc. In some examples different cloudenvironments may be associated with different organizations. Forinstance, the vehicle accident analyzer 804 may provide differentcustomized interfaces for different operators and/or user devices 802,which are configured to receive vehicle accident model configurationvariables based on the organization for which the new cloud-baseddeployment (e.g., a model training or model execution deployment) isrequested. Different organizations may have different predetermined setsof variable configuration data, and/or provide different interfaces tousers. For instance, an organization may be associated with a same setof administrators and/or may requires all cloud deployments to becreated via the same cloud service provider(s).

The data received via the accident model configuration interface 806 mayinclude any of the data described herein relating to training and/orexecuting machine learning models configured to determine injuryprobabilities based on vehicle accident data. After receiving the data,the vehicle accident analyzer 804 may determine and deploy one or morecloud-based systems to perform any or all of the functionalities of themodel training engine and/or model execution engine as described herein.In some examples, organizations may store and use pre-existing templatesdefining the specifications of new cloud-based environments for trainingand executing vehicle accident analysis models for the organization. Forinstance, cloud environment templates may include software instructionsthat can be directly executed by a cloud provisioning system. Forinstance, templates may be stored as configuration files (e.g.,infrastructure as code (IaC) configuration files) which may beexecutable and/or used by executable software to create and provisioncloud environments.

As shown in this example, the cloud provisioning system 808 may be usedto create and configure the requested cloud-based deployments fortraining and/or executing the various vehicle accident analysis modelsdescribed herein. The cloud provisioning system 808 using one or morecloud service providers 810-814 to deploy various systems within thecorresponding clouds 816-820. In some examples, the cloud provisioningsystem 808 may access and/or generate a storage space (e.g., arepository) for templates and/or other executable files corresponding tothe requested cloud deployments of vehicle accident analyzer models. Arepository for cloud-based systems may store a combination of thevariabilized template(s) generated based on the configuration datareceived from the user devices 802, as well as additionalnon-variabilized templates associated with the organization, theproject, the environment type, and/or the cloud service provider. Insome examples, a repository may be associated with a particular projector product development team, and different subfolders (or othersub-spaces) within the repository may store templates for differentenvironments (e.g., a production environment, various test environments,various research environments, etc.) associated with the project orproduct development team.

A requested deployment of a cloud-based systems may include deploymentsof model training systems and/or model execution systems for vehicleaccident analyzer models. In this example, each of the vehicleaccident-related data sources 822, model training engine 824, modeltraining data 826, model execution engine 828, and/or trained ML models830 may be similar or identical to the corresponding componentsdiscussed in above examples. Although in this example, each of thecomponents 822-830 stored and/or deployed within the cloud-basedenvironment 800 is depicted as residing in a single cloud, it can beunderstood that certain components (e.g., the model training engineand/or model execution engine) may be deployed across multiple differentclouds in some instances. To provision any or all of these cloud-baseddeployments, the cloud provisioning system 808 may determine andtransmit cloud provisioning instructions to the appropriate cloudservice providers 810-814. Such instructions may include infrastructureas code software instructions using a declarative configuration languagein a structured format. To generate and execute vehicle accidentanalysis models in cloud-based environments, the vehicle accidentanalyzer 804 may use languages including one or more of Terraform® byHashicorp®, Cloudformation® by AWS®, Azure Resource Manager® byMicrosoft Azure®, Cloud Deployment Manager® by Google®, OracleOrchestration Cloud® by Oracle®, or Ansible® by Redhat®. It can beunderstood from this disclosure that these structured format languagesare non-limiting examples only, and that any other domain-specificlanguage for provisioning cloud deployments may be used in otherexamples.

As illustrated by the various examples above, the techniques describedherein provide a number of technical advantages in operating vehicleaccident analysis systems, and more generally in training and executingmachine learning models in event-based computing infrastructures.Initially, the examples of training and executing machine learningmodels described herein provide improvements over existing systems bymore quickly and accurately identifying potentially injured partiesbased on vehicle accident data. The machine learning models describedherein, including but not limited to decision tree models with branchingcriteria based on vehicle accident data and injury ground truth data,may determine more accurate probability predictions for accidentinjuries, based on payloads that include combined data from various datasources. As a result, the vehicle accident analysis systems describedherein may improve overall vehicle safety and individual health outcomeswhen vehicle accidents occur, by detecting potential injuries earlierand more accurately than existing systems, even in situations when theinjuries may be unreported, concealed, and/or unknown to the otherparties involved in the accident. These systems and techniques alsoinclude improved and more efficient routing of potential injurynotifications and relevant accident data to medical care providers,enabling the injured parties to receive the appropriate medical caremore quickly than with existing systems. Similarly, these techniquesenable improved prioritization and triage of potential injuries based onthe likelihood and severity of the injuries, and also may moreeffectively determine false positives among the potential injuries(e.g., inaccurate injury data, exaggerated and/or unrelated injuryreports).

The techniques described herein also may improve the functioning andefficiency of computer systems that include execution engines formachine learning models. As described herein, a model execution enginemay be integrated with an event-driven system in which data events arereceived including updates to accident data records, and in which thedata events may trigger an execution (or re-execution) of thecorresponding trained model. Based on the types of accident data events,the timing of the received events, and/or the data sources from whichthe events are received, the vehicle accident analysis system mayimplement various delay timers that control the timing of a subsequentexecution of the model. For instance, the vehicle accident analysissystem can be configured to automatically re-execute a trained model anddetermine updated model output (e.g., an updated injury probability) inresponse to unique or significant changes to the data associated with avehicle accident, but may delay and defer performing a re-execution ofthe model in response to duplicate events and/or insignificant datachanges. Accordingly, the deduplication component and delay timersdescribed above can be configured to save computational resources andprovide improved efficiency by optimally determining when and how oftenthe machine learning model is to be re-executed in response to receivingupdated data events. Further, the improvements described herein formodel execution engines apply not only to the machine learning modelsconfigured to predict injury probabilities associated with vehicleaccidents, but may similarly apply to any number of additional machinelearning models having various structures and algorithms and configuredto perform predictive modeling in any number of different technicalfields.

Additionally or alternatively, these techniques may provide improvementsin the technical fields of device and/or server monitoring and workloadmanagement. As described above, a vehicle accident analysis system mayevaluate and apply injury probability thresholds for routingcommunications and providing processing instructions to various targetsystems. These probability thresholds may be adjusted dynamically basedon the current workloads and/or current available resources at eachtarget system. The vehicle accident analysis system may determineoptimal probability thresholds for each target system in order todistribute the numbers and types of tasks initiated on the targetsystems, and to control the corresponding processing loads handled bythe target systems. Coordinated configuration of the probabilitythresholds for the various target systems may improve the efficiency ofrouting various downstream processing tasks to an optimal target system,as well as providing a workload control mechanism to manage andcoordinate the processing workload across multiple target systems whichmay be unknown to one another.

In some instances, one or more components may be referred to herein as“configured to,” “configurable to,” “operable/operative to,”“adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Thoseskilled in the art will recognize that such terms (e.g., “configuredto”) can generally encompass active-state components and/orinactive-state components and/or standby-state components, unlesscontext requires otherwise.

As used herein, the term “based on” can be used synonymously with“based, at least in part, on” and “based at least partly on.”

As used herein, the terms “comprises/comprising/comprised” and“includes/including/included,” and their equivalents, can be usedinterchangeably. An apparatus, system, or method that “comprises A, B,and C” includes A, B, and C, but also can include other components(e.g., D) as well. That is, the apparatus, system, or method is notlimited to components A, B, and C.

While the invention is described with respect to the specific examples,it is to be understood that the scope of the invention is not limited tothese specific examples. Since other modifications and changes varied tofit particular operating requirements and environments will be apparentto those skilled in the art, the invention is not considered limited tothe example chosen for purposes of disclosure, and covers all changesand modifications which do not constitute departures from the truespirit and scope of this invention.

Although the application describes embodiments having specificstructural features and/or methodological acts, it is to be understoodthat the claims are not necessarily limited to the specific features oracts described. Rather, the specific features and acts are merelyillustrative some embodiments that fall within the scope of the claimsof the application.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a computer system and via a first data channel, dataassociated with a vehicle accident; generating, by the computer system,a data payload based at least in part on the data associated with thevehicle accident; providing, by the computer system, the data payload asinput to a machine learning model; receiving, by the computer system, anoutput from the machine learning model; determining, by the computersystem, an injury probability associated with the vehicle accident,based at least in part on the output from the machine learning model;determining, by the computer system, a target computer system of anentity associated with the vehicle accident, based at least in part onthe injury probability associated with the vehicle accident; andtransmitting, by the computer system, an instruction to the targetcomputer system to initiate a process.
 2. The computer-implementedmethod of claim 1, wherein the data associated with the vehicle accidentincludes at least one of: a statement from a party involved in thevehicle accident; location coordinates associated with the vehicleaccident; weather conditions associated with the vehicle accident; ortelematics data captured by a vehicle involved in the vehicle accident.3. The computer-implemented method of claim 1, wherein the machinelearning model comprises a decision tree model trained based onhistorical vehicle accident data.
 4. The computer-implemented method ofclaim 3, wherein the decision tree model includes a branching criteriabased on at least one of: a party to the vehicle accident associatedwith the first data channel; a time associated with receiving the data,relative to a time of the vehicle accident; or a keyword within adescription from a party of the vehicle accident.
 5. Thecomputer-implemented method of claim 1, further comprising: determiningan attribute associated with the vehicle accident, wherein the attributeincludes at least one of: geographic region associated with the vehicleaccident; a time period associated with the vehicle accident; or adriving condition associated with the vehicle accident; and selectingthe machine learning model from a plurality of models, based at least inpart on the attribute.
 6. The computer-implemented method of claim 1,further comprising: determining, based at least in part on the firstdata channel, a model execution delay time; initiating a timer based atleast in part on the model execution delay time; and executing themachine learning model based at least in part on a completion of thetime, wherein executing the machine learning model includes providingthe data payload as input to the machine learning model.
 7. Thecomputer-implemented method of claim 1, wherein determining the targetcomputer system comprises: determining a workload metric associated withthe target computer system, at a time associated with receiving thedata; determining a probability threshold based at least in part on theworkload metric; comparing the probability threshold to the injuryprobability; and determining the target computer system as a target forthe instruction, based at least in part on determining that the injuryprobability meets or exceeds the probability threshold.
 8. A computersystem, comprising: one or more processors; and one or morenon-transitory computer-readable media storing computer-executableinstructions that, when executed by the one or more processors, causethe one or more processors to perform operations comprising: receiving,via a first data channel, data associated with a vehicle accident;retrieving, from an accident data store, previous data associated withthe vehicle accident; generating a payload based at least in part on thedata associated with the vehicle accident and the previous dataassociated with the vehicle accident; executing a machine learningmodel, the executing comprising providing the payload as input to themachine learning model; determining an injury probability associatedwith the vehicle accident, based at least in part on an output from themachine learning model; determining a target computer system of anentity associated with the vehicle accident, based at least in part onthe injury probability; and transmitting an instruction to the targetcomputer system to initiate a process.
 9. The computer system of claim8, wherein the data associated with the vehicle accident includes atleast one of: a statement from a party involved in the vehicle accident;location coordinates associated with the vehicle accident; weatherconditions associated with the vehicle accident; or telematics datacaptured by a vehicle involved in the vehicle accident.
 10. The computersystem of claim 8, wherein the machine learning model comprises adecision tree model trained based on historical vehicle accident data.11. The computer system of claim 10, wherein the decision tree modelincludes a branching criteria based on at least one of: a party to thevehicle accident associated with the first data channel; a timeassociated with receiving the data, relative to a time of the vehicleaccident; or a keyword within a description from a party of the vehicleaccident.
 12. The computer system of claim 8, the operations furthercomprising: determining an attribute associated with the vehicleaccident, wherein the attribute includes at least one of: geographicregion associated with the vehicle accident; a time period associatedwith the vehicle accident; or a driving condition associated with thevehicle accident; and selecting the machine learning model from aplurality of models, based at least in part on the attribute.
 13. Thecomputer system of claim 8, the operations further comprising:determining, based at least in part on the first data channel, a modelexecution delay time; initiating a timer based at least in part on themodel execution delay time; and executing the machine learning modelbased at least in part on a completion of the time, wherein executingthe machine learning model includes providing a data payload as input tothe machine learning model.
 14. The computer system of claim 8, whereindetermining the target computer system comprises: determining a workloadmetric associated with the target computer system, at a time associatedwith receiving the data; determining a probability threshold based atleast in part on the workload metric; comparing the probabilitythreshold to the injury probability; and determining the target computersystem as a target for the instruction, based at least in part ondetermining that the injury probability meets or exceeds the probabilitythreshold.
 15. One or more non-transitory computer-readable mediastoring instructions executable by a processor, wherein theinstructions, when executed by the processor, cause the processor toperform operations comprising: receiving, via a first data channel,first data associated with a vehicle accident; receiving, via a seconddata channel different from the first data channel, second dataassociated with the vehicle accident; determining input data based atleast in part on the first data and the second data; providing the inputdata as input to a machine learning model; determining an injuryprobability associated with the vehicle accident, based at least in parton an output from the machine learning model; determining a targetcomputer system of an entity associated with the vehicle accident, basedat least in part on the injury probability; and transmitting aninstruction to the target computer system to initiate a processassociated with the vehicle accident.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein the first data associatedwith the vehicle accident includes a statement from a party involved inthe vehicle accident, and the second data associated with the vehicleaccident includes at least one of: location coordinates associated withthe vehicle accident; weather conditions associated with the vehicleaccident; or telematics data captured by a vehicle involved in thevehicle accident.
 17. The one or more non-transitory computer-readablemedia of claim 15, wherein the machine learning model comprises adecision tree model trained based on historical vehicle accident data.18. The one or more non-transitory computer-readable media of claim 15,the operations further comprising: determining an attribute associatedwith the vehicle accident, wherein the attribute includes at least oneof: geographic region associated with the vehicle accident; a timeperiod associated with the vehicle accident; or a driving conditionassociated with the vehicle accident; and selecting the machine learningmodel from a plurality of models, based at least in part on theattribute.
 19. The one or more non-transitory computer-readable media ofclaim 15, the operations further comprising: determining, based at leastin part on the first data channel, a model execution delay time;initiating a timer based at least in part on the model execution delaytime; and executing the machine learning model based at least in part ona completion of the time, wherein executing the machine learning modelincludes providing the input data as input to the machine learningmodel.
 20. The one or more non-transitory computer-readable media ofclaim 15, wherein determining the target computer system comprises:determining a workload metric associated with the target computersystem, at a time associated with receiving the first data; determininga probability threshold based at least in part on the workload metric;comparing the probability threshold to the injury probability; anddetermining the target computer system as a target for the instruction,based at least in part on determining that the injury probability meetsor exceeds the probability threshold.